Programmed cell death ligand 1 cut-point is associated with reduced disease specific survival in resected pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Programmed cell death 1 (PD1) inhibitors have recently shown promising anti-cancer effects in a number of solid tumor types. A predictive biomarker to this class of drugs has not been clearly identified; however, overexpression of the PD1 ligand (PD-L1) has shown particular promise in lung adenocarcinoma. In this study, we explore the staining characteristics, prevalence, and clinico-molecular correlates of PD-L1 overexpression in pancreatic ductal adenocarcinoma (PDAC). METHODS: A tissue microarray (TMA) was constructed from cases of resected PDAC. PD-L1 immunohistochemistry (IHC) was performed using the SP142 primary antibody. Immunohistochemical assessment for deficient mismatch repair status (MMRd), CD3 and CD8 were performed. All biomarkers were assessed independently by two anatomical pathologists and consensus achieved on all cases. Survival analysis was performed using three thresholds (> = 1%, >5% and >10%) for tumor cell membrane staining. RESULTS: Two-hundred fifty-two cases were included in the TMA and evaluable by IHC. Thirty-one (12%), 17 (7%), 12(5%) cases were positive at percentage cut offs of >0, >5, and >10% respectively. Increased PD-L1 expression was associated with inferior prognosis (p = 0.0367). No statistically significant association was identified between PD-L1 status and MMR status or tumor infiltrating lymphocytes. CONCLUSIONS: This data suggests that there is an inverse relationship between PD-L1 expression and disease specific survival times in resected PDAC. Consequently, this association may represent a phenotype where increased PD-L1 expression has an effect on tumor biology and could therefore identify a subgroup where PD1 blockade could have enhanced effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".